Lets say I have following pandas DataFrame:

import pandas as pd
df = pd.DataFrame({"A":[1,pd.np.nan,2], "B":[5,6,0]})

Which would look like:

>>> df
     A  B
0  1.0  5
1  NaN  6
2  2.0  0

First option

I know one way to check if a particular value is NaN, which is as follows:

>>> df.isnull().ix[1,0]
True

Second option (not working)

I thought below option, using ix, would work as well, but it’s not:

>>> df.ix[1,0]==pd.np.nan
False

I also tried iloc with same results:

>>> df.iloc[1,0]==pd.np.nan
False

However if I check for those values using ix or iloc I get:

>>> df.ix[1,0]
nan
>>> df.iloc[1,0]
nan

So, why is the second option not working? Is it possible to check for NaN values using ix or iloc?

Try this:

In [107]: pd.isnull(df.iloc[1,0])
Out[107]: True

UPDATE: in a newer Pandas versions use pd.isna():

In [7]: pd.isna(df.iloc[1,0])
Out[7]: True

The above answer is excellent. Here is the same with an example for better understanding.

>>> import pandas as pd
>>>
>>> import numpy as np
>>>
>>> pd.Series([np.nan, 34, 56])
0     NaN
1    34.0
2    56.0
dtype: float64
>>>
>>> s = pd.Series([np.nan, 34, 56])
>>> pd.isnull(s[0])
True
>>>

I also tried couple of times, the following trials did not work. Thanks to @MaxU.

>>> s[0]
nan
>>>
>>> s[0] == np.nan
False
>>>
>>> s[0] is np.nan
False
>>>
>>> s[0] == 'nan'
False
>>>
>>> s[0] == pd.np.nan
False
>>>

pd.isna(cell_value) can be used to check if a given cell value is nan. Alternatively, pd.notna(cell_value) to check the opposite.

From source code of pandas:

def isna(obj):
    """
    Detect missing values for an array-like object.

    This function takes a scalar or array-like object and indicates
    whether values are missing (``NaN`` in numeric arrays, ``None`` or ``NaN``
    in object arrays, ``NaT`` in datetimelike).

    Parameters
    ----------
    obj : scalar or array-like
        Object to check for null or missing values.

    Returns
    -------
    bool or array-like of bool
        For scalar input, returns a scalar boolean.
        For array input, returns an array of boolean indicating whether each
        corresponding element is missing.

    See Also
    --------
    notna : Boolean inverse of pandas.isna.
    Series.isna : Detect missing values in a Series.
    DataFrame.isna : Detect missing values in a DataFrame.
    Index.isna : Detect missing values in an Index.

    Examples
    --------
    Scalar arguments (including strings) result in a scalar boolean.

    >>> pd.isna('dog')
    False

    >>> pd.isna(np.nan)
    True

I made up some workaround:

x = [np.nan]

In [4]: x[0] == np.nan
Out[4]: False

but:

In [5]: np.nan in x
Out[5]: True

You can see list contain method implementation, to understand why it works.